Mining Express Service Innovation Opportunity From Online Reviews

被引:19
作者
Zhang, Ning [1 ]
Zhang, Rui [2 ]
Pang, Zhiliang [2 ]
Liu, Xue [2 ]
Zhao, Wenfei [2 ]
机构
[1] Qingdao Univ, Business Sch, Qingdao, Peoples R China
[2] Qingdao Univ, Qingdao, Peoples R China
关键词
Express Service; Online Reviews; Opportunity Algorithm; Service Innovation; Text Mining; FEATURE-SELECTION; PRODUCT;
D O I
10.4018/JOEUC.20211101.oa3
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In order to further meet the diversified needs of customers and enhance market competitiveness, it is necessary for express delivery enterprises to carry out service innovation. From the perspective of customer demand, this paper proposes a framework for mining service innovation opportunities. This framework uses text mining to analyze user-generated content and tries to provide a scientific service innovation scheme for express enterprises. Firstly, the authors crawl online reviews of express companies and use LDA model to identify service attributes. Secondly, customer satisfaction is calculated by sentiment analysis, and simultaneously, the importance of each service attribute is calculated. Finally, the authors carry out an opportunity algorithm with the results of text mining to quantify the innovation opportunities of service attributes. The results show that the framework can effectively identify service innovation opportunities from the perspective of customer demand. This study provides a new way to explore the direction of service innovation from the perspective of customer demand.
引用
收藏
页数:15
相关论文
共 50 条
  • [1] Determining banking service attributes from online reviews: text mining and sentiment analysis
    Mittal, Divya
    Agrawal, Shiv Ratan
    INTERNATIONAL JOURNAL OF BANK MARKETING, 2022, 40 (03) : 558 - 577
  • [2] Sourcing product innovation intelligence from online reviews
    Goldberg, David M.
    Abrahams, Alan S.
    DECISION SUPPORT SYSTEMS, 2022, 157
  • [3] Understanding the Order Effect of Online Reviews: A Text Mining Perspective
    Tripathi, Sambit
    Deokar, Amit, V
    Ajjan, Haya
    INFORMATION SYSTEMS FRONTIERS, 2022, 24 (06) : 1971 - 1988
  • [4] Mining Online Hotel Reviews: A Case Study from Hotels in China
    Tian, Xin
    He, Wu
    Tao, Ran
    Akula, Vasudeva
    AMCIS 2016 PROCEEDINGS, 2016,
  • [5] Mining Online Book Reviews for Sentimental Clustering
    Lin, Eric
    Fang, Shiaofen
    Wang, Jie
    2013 IEEE 27TH INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION NETWORKING AND APPLICATIONS WORKSHOPS (WAINA), 2013, : 179 - 184
  • [6] Improving Service Quality Using Text Mining and Sentiment Analysis of Online Reviews
    Chalupa, Stepan
    Petricek, Martin
    Chadt, Karel
    QUALITY-ACCESS TO SUCCESS, 2021, 22 (182): : 46 - 49
  • [7] A text mining study of online reviews to understand intercity bus service quality
    Hussain, Atif
    Shafiq, Adnan
    Awan, Muhammad Usman
    Hashmi, Junaid Iqbal
    TRANSPORT POLICY, 2025, 162 : 325 - 335
  • [8] Text Mining Online Reviews: What Makes a Helpful Online Review?
    Kim R.Y.
    IEEE Engineering Management Review, 2023, 51 (04): : 145 - 156
  • [9] Opinion Mining from Online Reviews in Bali Tourist Area
    Prameswari, Puteri
    Surjandari, Isti
    Laoh, Enrico
    2017 3RD INTERNATIONAL CONFERENCE ON SCIENCE IN INFORMATION TECHNOLOGY (ICSITECH), 2017, : 226 - 230
  • [10] COMPETITIVE ANALYSIS OF ONLINE REVIEWS USING EXPLORATORY TEXT MINING
    Amadio, William J.
    Procaccino, J. Drew
    TOURISM AND HOSPITALITY MANAGEMENT-CROATIA, 2016, 22 (02): : 193 - 210